{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=10000, noise=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 2744 candidates, totalling 8232 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
      "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:    2.7s\n",
      "[Parallel(n_jobs=3)]: Done 1764 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=3)]: Done 6084 tasks      | elapsed:   20.8s\n",
      "[Parallel(n_jobs=3)]: Done 8232 out of 8232 | elapsed:   27.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort=False, random_state=None,\n",
       "                                              splitter='best'),\n",
       "             iid='warn', n_jobs=3,\n",
       "             param_grid=[{'max_depth': range(2, 30),\n",
       "                          'max_leaf_nodes': range(2, 100)}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=5)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid =[\n",
    "    {'max_depth': range(2, 30, 1), 'max_leaf_nodes': range(2,100,1)},\n",
    "]\n",
    "tree_clf = DecisionTreeClassifier()\n",
    "grid_search = GridSearchCV(tree_clf, param_grid, cv=3,\n",
    "                           scoring = 'accuracy',\n",
    "                           n_jobs = 3,\n",
    "                           verbose = 5)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.854250 using {'max_depth': 7, 'max_leaf_nodes': 15}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best: %f using %s\" % (grid_search.best_score_,grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=7,\n",
       "                       max_features=None, max_leaf_nodes=15,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.841875 {'max_depth': 29, 'max_leaf_nodes': 68}\n",
      "0.8415 {'max_depth': 29, 'max_leaf_nodes': 69}\n",
      "0.841375 {'max_depth': 29, 'max_leaf_nodes': 70}\n",
      "0.8415 {'max_depth': 29, 'max_leaf_nodes': 71}\n",
      "0.8415 {'max_depth': 29, 'max_leaf_nodes': 72}\n",
      "0.841625 {'max_depth': 29, 'max_leaf_nodes': 73}\n",
      "0.843 {'max_depth': 29, 'max_leaf_nodes': 74}\n",
      "0.84275 {'max_depth': 29, 'max_leaf_nodes': 75}\n",
      "0.84275 {'max_depth': 29, 'max_leaf_nodes': 76}\n",
      "0.842125 {'max_depth': 29, 'max_leaf_nodes': 77}\n",
      "0.841875 {'max_depth': 29, 'max_leaf_nodes': 78}\n",
      "0.841875 {'max_depth': 29, 'max_leaf_nodes': 79}\n",
      "0.8425 {'max_depth': 29, 'max_leaf_nodes': 80}\n",
      "0.841875 {'max_depth': 29, 'max_leaf_nodes': 81}\n",
      "0.84225 {'max_depth': 29, 'max_leaf_nodes': 82}\n",
      "0.842 {'max_depth': 29, 'max_leaf_nodes': 83}\n",
      "0.841375 {'max_depth': 29, 'max_leaf_nodes': 84}\n",
      "0.840875 {'max_depth': 29, 'max_leaf_nodes': 85}\n",
      "0.841125 {'max_depth': 29, 'max_leaf_nodes': 86}\n",
      "0.841125 {'max_depth': 29, 'max_leaf_nodes': 87}\n",
      "0.8405 {'max_depth': 29, 'max_leaf_nodes': 88}\n",
      "0.8405 {'max_depth': 29, 'max_leaf_nodes': 89}\n",
      "0.8405 {'max_depth': 29, 'max_leaf_nodes': 90}\n",
      "0.840625 {'max_depth': 29, 'max_leaf_nodes': 91}\n",
      "0.840375 {'max_depth': 29, 'max_leaf_nodes': 92}\n",
      "0.840125 {'max_depth': 29, 'max_leaf_nodes': 93}\n",
      "0.84025 {'max_depth': 29, 'max_leaf_nodes': 94}\n",
      "0.839875 {'max_depth': 29, 'max_leaf_nodes': 95}\n",
      "0.83925 {'max_depth': 29, 'max_leaf_nodes': 96}\n",
      "0.839375 {'max_depth': 29, 'max_leaf_nodes': 97}\n",
      "0.83875 {'max_depth': 29, 'max_leaf_nodes': 98}\n",
      "0.83825 {'max_depth': 29, 'max_leaf_nodes': 99}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "cvres = grid_search.cv_results_\n",
    "means = grid_search.cv_results_['mean_test_score']\n",
    "params = grid_search.cv_results_['params']\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(mean_score, params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for plotting\n",
    "list_a = [i['max_depth'] for i in grid_search.cv_results_['params']]\n",
    "list_b = [i['max_leaf_nodes'] for i in grid_search.cv_results_['params']]\n",
    "t = list_b\n",
    "s = list_a\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(s, t)\n",
    "# ax.plot(t)\n",
    "ax.set(xlabel='max_depth', ylabel='mean_test_score',\n",
    "       title='Best Superparams')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# %matplotlib inline\n",
    "fig,axarr = plt.subplots()\n",
    "ax =axarr\n",
    "x = list_b\n",
    "y = means\n",
    "s = list_a # 散点大小\n",
    "c = np.random.rand(len(means)) # 随机颜色\n",
    "ax.scatter(x, y, s=s, c=c, alpha=0.5)\n",
    "ax.set(xlabel='max_leaf_nodes', ylabel='mean_test_score',\n",
    "       title='Best Superparams')\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "tree_clf = DecisionTreeClassifier(max_depth=7, max_leaf_nodes=15)\n",
    "tree_clf.fit(X, y)\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"moons_tree.dot\",\n",
    "#     feature_names=iris.feature_names[2:],\n",
    "#     class_names=iris.target_names,\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试集得分\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.854250 using {'max_depth': 7, 'max_leaf_nodes': 15}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best: %f using %s\" % (grid_search.best_score_,grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test DataSets Score： 0.86\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree_clf = DecisionTreeClassifier(max_depth=7, max_leaf_nodes=15)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "print(\"Test DataSets Score：\", tree_clf.score(X_test, y_test)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "rs = ShuffleSplit(n_splits=1000, test_size=0.25, random_state=0, train_size=None)\n",
    "list_predict = []\n",
    "list_score = []\n",
    "for train_index, test_index in rs.split(X_train, y_train):\n",
    "    tree_clf.fit(X[train_index], y[train_index])\n",
    "    y_predict = tree_clf.predict(X_test)\n",
    "    list_predict.append(y_predict)\n",
    "    list_score.append(tree_clf.score(X_test, y_test))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "at = np.array(list_predict)\n",
    "tt = at.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 1, ..., 1, 0, 1]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import mode\n",
    "en = mode(at)\n",
    "y_predict = mode(en)[0][0]\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t准确率: 87.00%\n"
     ]
    }
   ],
   "source": [
    "c = np.count_nonzero(y_predict == y_test) \n",
    "print('\\t准确率: %.2f%%' % (100 * float(c) / float(len(y_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
